# AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CT

arXiv: https://arxiv.org/abs/2209.01212 · HTML: https://mlsanity.com/paper/2209.01212/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 3 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)

## Built on by (most cited first, 5 of 5)

- [MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output](https://mlsanity.com/paper/2510.10011/) (2025)
- [Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2303.07126/) (2023)
- [Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images](https://mlsanity.com/paper/2303.06942/) (2023)
- [Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies](https://mlsanity.com/paper/2312.04344/) (2023)
- [AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net](https://mlsanity.com/paper/2309.12114/) (2023)

## Abstract (arXiv)

Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesion with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel training strategy to build deep learning models capable of systemic tumor segmentation. Our method is validated on the training set of the AutoPET 2022 Challenge. We achieved 0.7574 Dice score, 0.0299 false positive volume and 0.2538 false negative volume on preliminary test set.The code of our work is available on the following link: https://github.com/ZZZsn/MICCAI2022-autopet.